In this paper, we introduced a new framework of speech recognizer based on HMM and neural net. Unlike the traditional hybrid system, the neural net was used as a post processor, which classify the speech data segmented by HMM recognizer. The purpose of this method is to improve the top-choice accuracy of HMM based speech recognition system in our lab. Major issues such as how to use the segment...

This paper proposes an approach to construct a better Semantic Perceptron Net (SPN) used for topic spotting. To accomplish this task a learning paradigm call: neural network ensembling is used. Applying this technique to the original structure of Semantic Perceptron Net a new system called GA-SPN (Genetic Algorithm based Semantic Perceptron Net) was developed. The new system uses a neural netwo...

Neural networks have recently re-emerged as a powerful hypothesis class, yielding impressive classification accuracy in multiple domains. However, their training is a non-convex optimization problem which poses theoretical and practical challenges. Here we address this difficulty by turning to “improper” learning of neural nets. In other words, we learn a classifier that is not a neural net but...

This paper describes the implementation of a fast neural net simulator on a novel parallel distributed-memory computer. A 60-processor system, named MUSIC, 1 is operational and runs the back-propagation algorithm at a speed of 247 million connection updates per second (continuous weight update) using 32 bit oating-point precision. This is equal to 1 GGops sustained performance. The complete sys...

In medical image segmentation, the neural network structure of U-Net family has demonstrated sufficient advantages. However, MRI images have different scan parameters and times, resulting in feature representation images. Furthermore, there is a great class imbalance between bone cartilage tissues knee To address these issues, region-based two-stage tissue segmentation proposed this paper. The ...

In this paper we address the problem of constructing reliable neural-net implementations, given the assumption that any particular implementation will not be totally correct. The approach taken in this paper is to organize the inevitable errors so as to minimize their impact in the context of a multiversion system, i.e., the system functionality is reproduced in multiple versions, which togethe...

FOR SPEECH SIGNAL PROCESSING Stéphane Durand and Frédéric Alexandre Crin-CNRS / Inria Lorraine BP 239, F-54506 Vand÷uvre-les-Nancy Cedex Nancy, France [email protected] , [email protected] ABSTRACT The neural net model TOM (Temporal Organization Map) that we present in the paper is a new connectionist approach whose time representation is di erent from the one in classical temporal connectionist mod...

Most work in evolutionary robotics used a neural net approach for control of a mobile robot. Genetic programming has mostly been used for computer simulations. We wanted to see if genetic programming is capable to evolve a hierarchical control architecture for simple reactive navigation on a large physical mobile robot. First, we evolved hierarchical control algorithms for a mobile robot using ...